function [InputWeight_temp, InputBias_temp] = generate_my_weights(data_train, num_attain_node, is, num_neighbor)
    num_train = size(data_train,1);
    rand_col = randperm(num_train, 1); % 随机抽取一列
    indices_list = generate_start_indices(num_train, num_attain_node); % 在此列的距离列表中抽取一组数
    random_indices = is(indices_list, rand_col);
    rand_label = data_train(random_indices, 1);

    Deactivation_ratio = 0.2;
    CDELM_threshold = 0.04;
    [CDELM_Weight, CDELM_bias] = generate_CDELM_Weight(data_train, random_indices, rand_label, CDELM_threshold,Deactivation_ratio);

    SELM_threshold = 0.04;
    [SELM_Weight, SELM_bias] = generate_SELM_Weight(data_train, random_indices, SELM_threshold,Deactivation_ratio);

    CSELM_threshold = 0.04;
    [CSELM_Weight, CSELM_bias] = generate_CSELM_Weight(data_train, rand_label, random_indices, CSELM_threshold,Deactivation_ratio);

    RSELM_threshold = 0.04;
    [RSELM_Weight, RSELM_bias] = generate_RSELM_Weight(data_train, rand_label, random_indices, RSELM_threshold,Deactivation_ratio);

%     [CDDM_Weight, CDDM_bias] = generate_CDDM_Weight(data_train, random_indices, is, num_neighbor);

%     InputWeight_temp = [CDELM_Weight; SELM_Weight; CSELM_Weight; RSELM_Weight; CDDM_Weight];
%     InputBias_temp = [CDELM_bias, SELM_bias, CSELM_bias, RSELM_bias, CDDM_bias]';
    InputWeight_temp = [CDELM_Weight; SELM_Weight; CSELM_Weight; RSELM_Weight];
    InputBias_temp = [CDELM_bias, SELM_bias, CSELM_bias, RSELM_bias]';
end
